CN116803784A

CN116803784A - Autonomous control in dense vehicle environments

Info

Publication number: CN116803784A
Application number: CN202310775555.2A
Authority: CN
Inventors: 马特·Y·鲁普; 杰拉尔德·H·恩格尔曼; 亚历克斯·莫里斯·米勒; 蒂莫西·D·兹维基; 勒瓦瑟·特里斯; 理查德·李·斯蒂芬森
Original assignee: Ford Global Technologies LLC
Current assignee: Ford Global Technologies LLC
Priority date: 2014-02-14
Filing date: 2015-02-13
Publication date: 2023-09-26
Also published as: DE102015202367A1; US9079587B1; RU2015104551A3; RU2015104551A; CN104843001A; MX342567B; GB2524384A; MX2015001842A; GB201502281D0

Abstract

The present application provides a computer in a first vehicle programmed to receive a first set of data from at least one sensor in the first vehicle and a second set of data from at least one second vehicle. The second set of data is from at least one sensor in at least one second vehicle. The computer is further programmed to use both the first set of data and the second set of data to resolve at least one characteristic of the road being traversed by the first vehicle.

Description

Autonomous control in dense vehicle environments

The application is a divisional application of an application patent application with the application number of 201510079208.1 and the application date of 2015, 02 and 13, which is filed by Ford Global technology company and has the application name of autonomous control in a dense vehicle environment.

Background

Vehicles, particularly vehicles being operated automatically or semi-automatically, may obtain data regarding ambient conditions through a variety of mechanisms, such as sensors included in the vehicle, and the like. The sensor data may provide information about environmental conditions, road edges, or lanes in the road, etc., and may be used to regulate proper speed of the vehicle, proper path of the vehicle, etc. However, existing vehicle sensor data is limited by limitations associated with information that may be determined therefrom. For example, vehicle sensors may be limited in their ability to provide data for automatically or semi-automatically operating the vehicle. This may be particularly true in dense environments, i.e., areas where multiple vehicles share roads and often affect the vision, navigation capabilities, etc. of other vehicles.

Disclosure of Invention

According to the present application there is provided a method performed in a computer in a first vehicle, the computer being arranged to operate the vehicle in at least one of an automatic and semi-automatic mode, the method comprising:

receiving a first set of data from at least one sensor in a first vehicle;

receiving a second set of data from at least one second vehicle, wherein the second set of data is from at least one sensor in the at least one second vehicle; and is also provided with

Both the first set of data and the second set of data are used to resolve at least one characteristic of a road being traversed by the first vehicle.

According to one embodiment of the application, it further includes using both the first set of data and the second set of data including the resolved at least one feature to determine at least one automatic activity for the first vehicle.

According to one embodiment of the application, wherein the at least one automatic activity is at least one of braking, accelerating, changing lanes, and changing distance from a vehicle in front.

According to one embodiment of the application, wherein the at least one characteristic resolved comprises one or more of available driving lanes, curvature of the road, slope of the road, highway entrance ramp, obstacle, weather conditions and road surface conditions.

According to one embodiment of the application, wherein the at least one characteristic resolved comprises at least one target comprising one of an object near the road and the at least one second vehicle.

According to one embodiment of the application, the at least one object comprises at least one third vehicle.

According to one embodiment of the application, it further comprises constructing a virtual map of the environment surrounding the first vehicle, the virtual map comprising the resolved at least one feature.

According to one embodiment of the application, wherein the second set of data from the at least one second vehicle comprises at least one of radar data, lidar data and picture data.

According to one embodiment of the application, some, but not all, of the second set of data is provided in Dedicated Short Range Communications (DSRC).

According to the present application there is provided a system comprising a computer in a vehicle, the computer comprising a processor and a memory, wherein the computer program is arranged to:

receiving a first set of data from at least one sensor in the first vehicle;

receiving a second set of data from at least one second vehicle, wherein the second set of data is from at least one sensor in the at least one second vehicle; and

both the first set of data and the second set of data are used to resolve at least one feature being traversed by the first vehicle.

According to an embodiment of the application, the computer program is further arranged to use both the first set of data and the second set of data comprising the resolved at least one feature to determine the at least one automatic activity for the first vehicle.

According to one embodiment of the application, wherein the at least one automatic activity is at least one of braking, accelerating, lane changing and changing distance from a vehicle in front.

According to one embodiment of the application, wherein the at least one characteristic resolved comprises at least one target comprising one of an object near the road and at least one second vehicle.

According to one embodiment of the application, the computer is further programmed to construct a virtual map of the environment surrounding the first vehicle, the virtual map comprising the resolved at least one feature.

According to one embodiment of the application, some, but not all, of the second set of data is provided in Dedicated Short Range Communication (DSRC) information.

Drawings

FIG. 1 is a block diagram of an exemplary automatic vehicle sensing system.

Fig. 2 is a block diagram of a vehicle road.

FIG. 3 is a diagram of an exemplary process for an automated vehicle in a dense environment.

Detailed Description

FIG. 1 is a block diagram of an exemplary automated vehicle system 100 in a dense environment, i.e., a roadway or the like including more than one vehicle 101, such as interstate highways, city roads, and the like. The computing device 105 in the vehicle 101 typically receives the collected data 115 from the one or more data collectors 110 and/or the one or more second vehicles 101 via one or more information 116. The computing device 105 further includes an autopilot module 106, for example, as a processor stored in the computing device 105 and executable by the computing device 105. The collected data 115 may be used by the first vehicle 101 computer 105 to make decisions regarding the operation of the vehicle 101, including operation of the vehicle 101 in an automatic or semi-automatic mode.

The use of data from the information 116 may provide improved operation of the vehicle 101 compared to automatic or semi-automatic operation of the vehicle 101 based solely on the collected data 115 from the vehicle 101 data collector. For example, a data collector 110, such as a camera, radar, lidar, etc., on the front of the first vehicle 101 may be limited or obstructed by features of the second vehicle 101 and/or the road, such as curves, hillsides, obstacles, such as falling objects, enclosed lanes, building areas, etc. Accordingly, the one or more information 116 may provide information from the one or more second vehicles 101 to the vehicle 101 computer 105 to augment and/or replace the collected data 115, the second vehicle 101 being located in a location that provides data that is not available from the data collector 110 in the first vehicle 101 or is known to be more accurate than the data 115 in the first vehicle 101.

Thus, the system 100 may be used in dense environments, i.e. roads or the like comprising a plurality of vehicles 101. In general, the system 100 may assist in predicting a path that the vehicle 101 will travel based on the data 115 regarding the surrounding environment, assist in determining a path that the vehicle 101 should travel based on the data 115, and in resolving obstacles, anomalies, etc. in the surrounding environment of the vehicle 101, such as a road. The system 100 may thus be beneficial in that conditions in the environment surrounding the vehicle 101 do not reflect existing maps or may be stored in the vehicle 101 computer 105 memory and/or in what is known as an electronic field of view for navigation. For example, in a road construction area, several lanes available in the road, curvature ramps of the road, etc. may not be reflected by the stored map. Further, the sensor data collector 110 of the first vehicle 101 may be blocked or obstructed, for example, by traffic jams, precipitation, haze, etc., and thus cannot obtain the data 115 or at least cannot obtain the correct data 115. However, by using the data 115 from the one or more second vehicles 101, the first vehicle 101 may construct an electronic field of view, such as a virtual map reflecting phenomena presented in the surrounding environment of the vehicle, such as available driving lanes, actual road curvature, slopes, obstacles, etc.

Exemplary System elements

The vehicle 101 includes a vehicle computer 105 that generally includes a processor and memory, including one or more forms of computer-readable media, and storing instructions for execution by the processor including the various operations disclosed herein. For example, the computer 105 typically includes, and is capable of executing, instructions for selecting an automatic mode of operation, to adjust the automatic mode of operation, to change the automatic mode of operation of the vehicle 101, and so on.

Further, the computer 105 may include more than one computing device. The operations of the computer 105 described herein may be performed, for example, by one or more computing devices, such as a controller or the like included in the vehicle 101 for monitoring and/or controlling various vehicle components, such as an Engine Control Unit (ECU), a Transmission Control Unit (TCU), a Power Steering Control Unit (PSCU), or the like. Computer 105 is typically configured for communication over a Controller Area Network (CAN) bus or the like. The computer 105 may also have a connection to an on-board diagnostic connector (OBD II) or the computer 105 may be hardwired to a specific driver control interface or subsystem ECU I/O (input/output). The computer 105 may transmit information to and/or receive information from various devices in the vehicle, such as a controller, an actuator, a sensor, etc. including the data collector 110, via a CAN bus, an OBD II, and/or other wired or wireless mechanism. Alternatively or additionally, where the computer 105 actually contains multiple devices, the CAN bus or the like may be used for communication between the devices described as the computer 105 in the present disclosure.

Further, the computer 105 may include or be communicatively coupled to one or more Radio Frequency (RF) transceivers, receivers, and/or transmitters, and may be configured to communicate with the network 120 and/or other vehicles 101. For example, the computer 105 is typically configured to send information 116 (described further below) to the other vehicle 101 and receive information 116 from the other vehicle 101. Various techniques including hardware, communication protocols, etc. are known for vehicle communication. For example, the information 116 described herein may be transmitted and received in accordance with Dedicated Short Range Communications (DSRC) or the like. As is known, DSRC is a relatively low power operation in the short to medium wave range in the spectrum in the 5.9GHz band, which is specifically allocated by the federal government.

In general, the communications of the vehicle 101 computer 105 may include a variety of wired and/or wireless network technologies, such as mobile (cellular), bluetooth, wired and/or wireless packet networks, and the like. Further, instructions for receiving data, such as from one or more data collectors 110 and/or human-machine interfaces (HMIs), such as an Interactive Voice Response (IVR) system, a Graphical User Interface (GUI) including a touch screen, etc., are typically included in the computer 105, such as module 106. Further, the computer 105 is typically configured to obtain data regarding one or more other vehicles from one or more information 116.

The autopilot module 106 is typically included in instructions stored in the computer 105 and executed by the computer 105. Using data received from the computer, such as from the data collector 110, the server 125, etc., the module 106 can control various vehicle 101 components and/or operations without the driver operating the vehicle 101. For example, the module 106 may be used to steer the vehicle 101 speed, accelerate, decelerate, steer, distance between vehicles and/or amount of time between vehicles, minimum lane change gap between vehicles, minimum left turn across a path, time to arrival, minimum time to arrival at an intersection (no signal) across an intersection, etc. The module 106 may use the collected data 115 and/or data from one or more other vehicles 101 provided in the one or more information 116 in determining one or more actions to be taken when the vehicle 101 is in an automatic or semi-automatic mode.

The data collector 110 may comprise a variety of devices. For example, various controllers in the vehicle may operate as data collectors 110 to provide collected data 115, such as collected data 115 regarding vehicle speed, acceleration, etc., over a CAN bus. Further, sensors or the like, global Positioning System (GPS) devices or the like may be included in the vehicle and may be provided as a data collector 110 to provide data directly to the computer 105, for example, through a wired or wireless connection. The data collector 110 may also include sensors or the like, such as mid-wave and long-wave range sensors, for detecting and possibly also obtaining information from other conditions besides the markers 160 and the vehicle 101 as further described below. For example, the sensor data collector 110 may include mechanisms such as radio, radar, lidar, sonar, cameras, or other image capture devices that may be used to detect markers 160 and/or obtain other collected data 115 related to the automated operation of the vehicle 101, such as measuring the distance between the vehicle 101 and other vehicles or objects, to detect other vehicles or objects, and/or to detect road conditions, such as bends, potholes, inclinations, bumps, changes in gradients, and the like.

The memory of computer 105 typically stores collected data 115. The collected data 115 may include a variety of data collected in the vehicle 101 from the data collector 110, including data 115 obtained from one or more markers 160. Above and below, examples of the collected data 115 are provided, for example, with respect to the indicia 160, and further, the data 115 may also include data calculated thereby in the computer 105. In general, the collected data 115 may include data that may be aggregated by the collection device 110 and/or any data calculated from such data. Thus, the collected data 115 may include a variety of data 115 related to the operation and/or performance of the vehicle 101, and in particular, data related to the movement of the vehicle 101. For example, in addition to data 115 obtained from, for example, markers 160 discussed below, the collected data 115 may also include data related to vehicle 101 speed, acceleration, braking, lane changing and/or lane usage (e.g., on a particular road and/or type of road, such as an interstate highway), average distance from other vehicles at various speeds or speed ranges, and/or other data 115 related to vehicle 101 operation.

The information 116 may include a variety of data related to the operation of the vehicle 101. For example, the present specification published by the society of automotive engineers (Society of Automotive Engineers) for DSRC is provided for including vehicle 101 data in a wide variety of information 116, including vehicle 101 position (e.g., latitude and longitude), speed, heading, acceleration status, brake system status, transmission status, steering wheel position, and the like. However, the information 116 is not limited to data elements included in the DSRC standard or any other standard. For example, the information 116 may include a wide variety of collected data 115 obtained from the vehicle 101 data collector 110, such as camera pictures, radar or lidar data, data from infrared sensors, and the like. Thus, the first vehicle 101 may receive the collected data 115 from the second vehicle 101, whereby the first vehicle 101 computer 105 may use the collected data 115 from the second vehicle 101 as input to the automatic mode 106 in the first vehicle 101, i.e., to determine automatic or semi-automatic operation of the first vehicle 101.

Advantageously, the information 116 may include historical data 115 from the vehicle 101. That is, in addition to reporting data 115 in information 116 on a real-time or near real-time basis, vehicle 101 may include data 115 related to one or more time periods at or prior to the present time associated with information 116. For example, the information 116 may indicate the vehicle 101 location (e.g., latitude and longitude), speed, and heading at a plurality of times. Such data 115 may sometimes be referred to as "trace navigation" data because the past path and/or projected path of the vehicle 101 may be determined from such data 115. Further, the trace navigation data is not limited to the data 115 of the path of the vehicle 101 associated with providing such data 115. For example, the vehicle 101 may provide trace navigation data relating to a variety of phenomena that may be included in the collected data 115 and provided in the information 116, such as a location, speed, orientation, etc. of another vehicle detected by the information 116, a road, weather conditions such as precipitation, temperature, etc., and objects on the road, etc.

Network 120, as described above, represents one or more mechanisms by which vehicle computer 105 may communicate with remote server 125 and/or user server 150. Thus, network 120 may be one or more of a variety of wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and/or wireless (e.g., mobile, wireless, satellite, microwave, and radio frequency) communication mechanisms, and any desired network topology (or topologies when multiple communication mechanisms are utilized). Exemplary communication networks include wireless communication networks (e.g., using Bluetooth, IEEE 802.11, etc.), local Area Networks (LANs), and/or Wide Area Networks (WANs), including the Internet, which provides a data communication server.

The server 125 may be one or more computer servers, each generally including at least one processor and at least one memory storing instructions executable by the processor, including instructions for performing the various steps and processes described herein. The server 125 may include or be communicatively coupled to a data store 130 for storing the collected data 115 received from the one or more vehicles 101.

User device 150 may be any of a variety of computing devices including a processor and memory and communication capabilities. For example, the user device 150 may be a portable computer, tablet computer, smart phone, etc. that uses IEEE 802.11, bluetooth, and/or mobile communication protocols, including capabilities for wireless communication. Further, the user device 150 may use such communication capabilities to communicate over the network 120, including with the vehicle 101 computer 105. The user device 150 may communicate with the vehicle 101 computer 105 through other mechanisms, such as a network in the vehicle 101, known protocols such as bluetooth, etc. Accordingly, certain operations related to the data collector 110 described herein may be performed using the user device 150, such as sound resolution functions, cameras, global Positioning System (GPS) functions, etc., in the user device 150 that may be used to provide data 115 to the computer 105. Further, the user device 150 may be used to provide a human-machine interface (HMI) to the computer 105.

Exemplary Environment

Fig. 2 is a block diagram of a roadway 155 being traversed by a plurality of vehicles 101a, 101b, 101c, 01d, and 101 e. In general, as discussed above, the plurality of vehicles 101 may use the data collector 110 to obtain a variety of collected data 115 and communicate the variety of collected data 115 to other vehicles 101 through one or more information 116.

Thus, each vehicle 101 may obtain data 115 that it may not obtain. For example, the object 160 may enclose the roadway 155. The first vehicle 101a may receive one or more pieces of information 116 about such data 115 of the object 160 from one or more second vehicles 101, such information 115 being unavailable to the first vehicle 101 a. For example, as described in fig. 2, the second vehicle 101b may obstruct the first vehicle 101a from obtaining information about the object 160. As another example, the road 155 may be curved, change in elevation, present traffic obstructions, lane closures, etc., preventing the first vehicle 101 from obtaining information 115 that is used to identify and/or avoid objects that affect travel and/or characteristics of the road 155.

Exemplary Process flow

Fig. 3 is a diagram of an exemplary process of an automated vehicle 101 sensing system in an automated mode, which is generally performed in accordance with instructions in a first vehicle 101 computer 105.

The process 300 begins in block 305, where the vehicle 101 directs an autopilot operation. That is, the vehicle 101 is partially or fully automatically operated, i.e., partially or fully controlled by the autopilot module 160, which may be configured to operate the vehicle 101 in accordance with the collected data 115. For example, all vehicle 101 operations, such as steering, braking, operation, etc., may be controlled by a module 106 in the computer 105. It is also possible that the vehicle 101 may be operated in block 220 in a partially automatic mode, sometimes referred to as a semi-automatic mode (i.e., a partially manual mode, wherein some operations, such as braking, may be manually controlled by the driver, while other operations, including steering, may be controlled by the computer 105). Similarly, the module 106 may control the time that the vehicle 101 changes lanes. Further, the process 200 may begin at some point after the vehicle 101 driving operation begins, such as when manually initiated by a vehicle occupant through a user interface of the computer 105.

Vehicles 101 in an automatic or semi-automatic mode typically transmit and receive, or at least listen for, information 116. Moreover, it is to be appreciated that vehicles 101 that are not in an automatic or semi-automatic mode and/or do not include an automatic module 106 or lack some or all of the automatic operating capabilities may include an information collector 110 that obtains collected information 115 and provides information 116. The specific data elements included in the information 116 may be collected data 115 of the vehicle 101 according to a known standard or protocol, such as DSRC, but as also described above, may be included that is not included in any modern standard or protocol. For example, in addition to data 115 relating to location, speed, etc., the vehicle 101 may provide information 116 sensor data 115, such as radar, lidar, etc., data, pictures, sounds, etc.

In any event, in block 310 following block 305, the computer 105 determines whether it has received one or more information 116 from one or more second vehicles 101. If not, the process 300 returns to block 305. Alternatively, process 300 proceeds to block 315.

In each of blocks 315 and 330, described in the following sequence, the computer 105 determines, for example in block 106, whether any activity is appropriate in the vehicle 101 to perform semi-automatic or automatic operations. In making such a determination, the computer 105 performs the automated or semi-automated operations described above with respect to block 305, but additionally or alternatively serves as input for determining at least one automated operation data obtained from the one or more second vehicles 101 in the one or more information 116. The computer 105 may then perform the automatic operation thus determined. Possible automatic operations or activities include, by way of example and without limitation, braking, accelerating, lane changing, and changing the distance from the vehicle in front, i.e., the vehicle in front of the vehicle 101.

For example, assume that a first vehicle 101 follows a second vehicle 101 along a hilly road. Further, it is assumed that the first vehicle 101 operates the first vehicle 101 in at least a semi-autonomous mode, such as adaptive cruise control, which is being used. That is, the first vehicle 101 uses radar or the like to detect and measure the distance from the second vehicle 101. However, in hilly environments, particularly if the first vehicle 101 has a rigid suspension, the radar may not be able to effectively detect the second vehicle 101, such as when the first vehicle 101 is driving up a slope and the second vehicle 101 is at or above the top of the slope. Thus, the collected data 115 from the second vehicle 101, e.g., reporting the position, speed, orientation, etc., of the second vehicle may be included in the information 116 and used by the computer 105 in the first vehicle 101 to maintain a predetermined speed and/or a predetermined distance from the second vehicle 101. Further, the vehicle 101 computer 105 may receive information 116 from a plurality of second vehicles 101 as described above. Moreover, the computer 105 may use the data 115 from the plurality of second vehicles 101 when in an automatic or semi-automatic mode in regulating one or more activities. For example, the confidence of the automation module 106 may increase or each of the plurality of second vehicles may be reached to provide mutually consistent data 115, such as indicating objects 160 in the road 155, indicating specific conditions in the road 155, such as water, ice, etc., indicating a desired path in the road 155, such as changing lanes, to avoid objects 160, such as construction obstacles, etc.

Thus, in block 315, real-time collected data 115 is obtained, along with real-time or near real-time data received in one or more information 116. In addition, the computer 105 also analyzes information 116 of historical data, such as DSRC "trace navigation" data, and the like. That is, the computer 105 may use the data in the one or more information 116 to determine the location, speed, and/or other distribution, e.g., acceleration, deceleration, etc., of the one or more vehicles 101 in more than one point in time, i.e., time period.

Then, in block 320, the computer 105 in the first vehicle 101, which uses historical data or trace navigation data from the plurality of second vehicles 101 in addition to the real-time or near real-time data 115 collected in the first vehicle 101, may construct a so-called electronic view, which is a map of the environment surrounding the first vehicle 101, locally disposed in the vehicle 101, such as in the computer 105. For example, the vehicle 101 may rely on the stored map to navigate using a Global Positioning System (GPS) application. The map, for example stored in the computer 105 and/or in a memory associated with the devices in the vehicle 101, may include information about the road 155, a number of available lanes, the direction of traffic flow, intersections, entrance ramps, exit ramps, etc., and accordingly, such stored map may be used to construct an initial electronic view.

However, because the information from this electronic field of view relies on GPS to locate the position of the vehicle (101) relative to the infrastructure, position determination is limited by the performance of the GPS system within the vehicle 101. In many cases the accuracy of this mechanism will not allow positioning the vehicle 101 in the exact traffic lane on the road 155, or in some cases, extending substantially parallel or with exit/entrance/service/turning lanes in the vicinity of the road 155. Moreover, the information in such stored maps may not reflect the actual situation, such as when one or more objects 160 obstruct some or all of the roads 155, when a construction area is performed in the roads 155, and so forth. In these cases, the available travel lanes, intersections, available exit ramps and/or entrance ramps, etc. may not be as reflective as in the stored map. However, by using data 115 from the surrounding vehicle 101, such as position data, speed data, heading data, etc., the vehicle 101 computer 105 may construct a virtual map, such as an electronic view reflecting real conditions or characteristics of the environment surrounding the vehicle 101, such as the road 155. The virtual map so constructed may indicate available travel lanes, obstacles such as objects 160 in the road 155 (sometimes the objects 160 and/or the second vehicle 101 are referred to as "targets"), and other conditions such as weather conditions, road surface conditions-e.g., ice, trash, etc., affecting the presence of travel through the road 155, etc. Generally, as used herein, the "environment" surrounding the vehicle 101 may refer to a predetermined radius around the vehicle 101, e.g., 500 meters, 1000 meters, etc., and/or a predetermined distance in front of and/or behind the vehicle 101 on the road 155, etc.

Following block 320, in block 325, the first vehicle 101 computer 105 estimates a first vehicle 101 path based on the collected data 115 in the first vehicle 101. That is, using information about the speed, position, acceleration/deceleration, etc. of the first vehicle 101, the computer 105 may project a similar path for the first vehicle 101 with respect to the electronic field of view.

Following block 325, in block 330, the first vehicle 101 computer 105 analyzes, for example, the aggregate, electronic view of all regions-for example, maps, information that have been generated by fusion of the data 115 from the first vehicle 101 and one or more second vehicles 101 as described above with respect to block 320. That is, statistical data analysis techniques or the like are used to estimate possible environmental and target locations (e.g., according to the X, Y, Z coordinate system), velocity/acceleration vectors (e.g., again according to the X, Y, Z coordinate system), and upcoming or connected road/vehicle paths, similar paths, and the like.

Then, after block 330, in block 335, the computer 105 provides the predicted vehicle 101 path, possibly one or more target locations and/or paths, and environmental data, such as lane availability, road friction, etc., to any automatic and/or semi-automatic ones of the modules 106 and/or vehicles 101.

In block 340, after block 330, computer 105 determines whether process 300 should continue. For example, if the automatic driving operation is stopped and the driver resumes manual control, the process 300 may be stopped if the vehicle 101 is powered off, or the like. In any event, if the process 300 should not continue, the process 300 stops after block 310. Otherwise, the process 300 continues at block 305.

Conclusion(s)

Computing devices such as those discussed herein substantially each include instructions executable by one or more computing devices such as those identified above and for performing the blocks or steps of the processes described above. For example, the process blocks discussed above are embodied as computer-executable instructions.

Computer-executable instructions may be encoded or translated from a computer program produced using a variety of programming languages and/or techniques, including without limitation, and Java alone or in combination ^TM C, C ++, visual Basic, java Script, perl, HTML, etc. Typically, a processor (e.g., a microprocessor) receives instructions from, for example, a memory, a computer-readable medium, or the like, and executes the instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer-readable media. Files in a computing device are typically a collection of data stored on a computer readable medium, such as a storage medium, random access memory, or the like.

Computer-readable media include any medium that can be read by a computer and that participates in providing data (e.g., instructions). Such a medium may take many forms, including but not limited to, non-volatile media, and the like. Non-volatile media includes, for example, optical or magnetic disks and other durable memory. Volatile media includes Dynamic Random Access Memory (DRAM), which typically constitutes a main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read.

In the drawings, like reference numerals designate like elements. Further, some or all of these elements may be changed. With respect to the media, processes, systems, methods, etc. described herein, it should be understood that although the steps, etc. of such processes have been described as occurring according to some ordered sequence, such processes may also be performed using steps that are performed in an order other than the order described herein. It is further understood that certain steps may be performed concurrently, other steps may be added, or certain steps described herein may be omitted. In other words, the description of the processes herein is provided for the purpose of explaining certain embodiments, and should in no way be construed as limiting the claimed application.

Accordingly, it is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and applications other than the examples provided will be apparent to those skilled in the art upon reading the above description. The scope of the application should be determined, not with reference to the above description, but instead should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that further developments will occur in the arts discussed herein, and that the disclosed systems and methods will be incorporated into such further embodiments. In summary, it is to be understood that the application is capable of modification and variation and is limited only by the following claims.

All terms used in the claims are intended to be given their broadest reasonable constructions and their ordinary meaning as understood by those skilled in the art unless an explicit indication to the contrary is made herein. In particular, the use of singular articles such as "a," "the," "said," etc. should be understood to describe one or more of the elements specified unless the claims explicitly recite the contrary.

Claims

1. A method performed in a computer in a first vehicle, the computer being configured to operate the vehicle in an automatic mode, the method comprising:

receiving a first set of data from at least one sensor in a first vehicle;

receiving a plurality of sets of data, the plurality of sets of data including a second set of data and a third set of data received from a different second vehicle, wherein the second set of data and the third set of data are each from at least one sensor of the second vehicle; and is also provided with

Analyzing the first, second, and third sets of data and predicting a path of the first vehicle based on the analysis;

the second set of data and the third set of data further comprise automatic operation data for each of the second vehicles; and

when the automatic operation data provided by each of the second vehicles agree with each other, the first vehicle performs an automatic operation on the first vehicle using the at least one automatic operation data to perform at least one of the first vehicle paths.

2. The method of claim 1, further comprising:

and using the automatic operation data of each of the second vehicles for input of the first vehicle based on the determination of the automatic operation of the first vehicle.

3. The method of claim 1, further comprising:

at least one automatic activity for the first vehicle is determined based on the predicted path.

4. A method according to claim 3, wherein the at least one automatic activity is at least one of braking, accelerating, changing lanes, and changing distance from a vehicle in front.

5. The method of claim 1, further comprising analyzing historical data in the first, at least second, and third sets of data using statistical data analysis techniques, constructing a virtual map of an environment surrounding the first vehicle based on the analysis; wherein the virtual map is structured as an electronic field of view reflecting real conditions or features of the first vehicle surroundings.

6. The method of claim 5, the virtual map comprising at least one feature comprising available one or more lanes of travel, curvature of a road, slope of a road, highway entrance ramp, obstacle, weather condition, and road surface condition.

7. The method of claim 5, wherein the historical data includes location data, speed data, and direction of travel data of the first vehicle or the second vehicle over one or more past time periods.

8. The method of claim 5, wherein the at least one characteristic comprises at least one target comprising one of an object proximate to a roadway, each of the second vehicles.

9. A system comprising a computer in a vehicle, the computer being arranged to operate a first vehicle in an automatic mode and comprising a processor and a memory, wherein the computer program is arranged to:

receiving a first set of data from at least one sensor in the first vehicle;

receiving a plurality of sets of data, the plurality of sets of data including at least a second set of data and a third set of data received from different second vehicles, wherein the second set of data and the third set of data are each from at least one sensor in each second vehicle;

10. The system of claim 9, wherein the computer performs a determination that the first vehicle is operating automatically and receives the automatic operation data for each of the second vehicles for input to the first vehicle based on the determination.

11. The system of claim 9, wherein the computer program is further configured to determine at least one automatic activity for the first vehicle based on the virtual map.

12. The system of claim 11, wherein the at least one automatic activity is at least one of braking, accelerating, changing lanes, and changing distance from a vehicle in front.

13. The system of claim 9, wherein the computer analyzes historical data in the first, at least second, and third sets of data using statistical data analysis techniques, constructing a virtual map of an environment surrounding the first vehicle based on the analysis; wherein the virtual map is structured as an electronic field of view reflecting real conditions or features of the first vehicle surroundings.

14. The system of claim 13, the virtual map comprising at least one feature comprising available one or more travel lanes, curvature of a road, slope of a road, highway entrance ramp, obstacle, weather condition, and road surface condition.

15. The system of claim 13, wherein the historical data includes location data, speed data, and direction of travel data of the first vehicle or the second vehicle over one or more past time periods.

16. The system of claim 13, wherein the at least one characteristic comprises at least one target comprising one of an object proximate to a roadway, each of the second vehicles.